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- Contrastive Learning for Compact Single Image Dehazing925
This paper proposes a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively and develops a compact dehazing network based on autoencoder-like framework.
- Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification172
A new strategy, Variational Self-Distillation (VSD), is presented, which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it, and highlights the need to rethink the way to estimate mutual information.
- Not All Pixels Are Matched99
A dense contrastive learning framework (DCLNet) is proposed, which performs pixel-to-pixel dense alignment acting on the intermediate representations, rather than the final deep feature, reducing the modality gap from a new perspective.
- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
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- Unveiling the Power of CLIP in Unsupervised Visible-Infrared Person Re-Identification85
A new prompt learning paradigm for unsupervised visible-infrared person re-identification (USL-VI-ReID) by taking full advantage of the visual-text representation ability from CLIP is proposed and a new memory-swapping contrastive learning is proposed.
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- Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer70
E2 STR demonstrates that a regular-sized model is sufficient to achieve effective ICL capabilities in STR, and exhibits remarkable training-free adaptation in var-ious scenarios and outperforms even the fine-tuned state-of-the-art approaches on public benchmarks.
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- Towards Compact Single Image Super-Resolution via Contrastive Self-distillation61
A novel contrastive self-distillation (CSD) framework to simultaneously compress and accelerate various off-the-shelf SR models to improve the quality of SR images and PSNR/SSIM via explicit knowledge transfer is proposed.
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- GEOcc: Geometrically Enhanced 3D Occupancy Network With Implicit-Explicit Depth Fusion and Contextual Self-Supervision41
GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception achieves State-of-the-Art performance on the Occ3D-nuScenes dataset with the least image resolution needed and the most weightless image backbone compared with current models, marking an improvement of 3.3% due to the proposed contributions.
- Cross-Domain and Cross-Modal Knowledge Distillation in Domain Adaptation for 3D Semantic Segmentation41
This paper designs the multi-modal style transfer to convert source image and point cloud to target style and proposes a novel model (Dual-Cross) that integrates Cross-Domain knowledge distillation (CDKD) and Cross-Modal Knowledge Distillation (CMKD), which significantly outperforms both uni- modal and cross-modAL state-of-the-art methods.
- Boosting Night-Time Scene Parsing With Learnable Frequency39
This paper proposes to exploit the image frequency distributions for night-time scene parsing with a Learnable Frequency Encoder (LFE) to model the relationship between different frequency coefficients to measure all frequency components dynamically and proposes a Spatial Frequency Fusion module (SFF) that fuses both spatial and frequency information to guide the extraction of spatial context features.
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- Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification37
This work proposes a Progressive Contrastive Learning with Hard and Dynamic Prototypes method for USVI-ReID that generates the hard prototype by selecting the sample with the maximum distance from the cluster center and theoretically shows that the hard prototype is used in the contrastive loss to emphasize divergence.
- Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning32
This paper designs a dual-branch network equipped with an active labeling strategy to maximize the power of tiny parts of labels and to directly realize 2D-to-3D knowledge transfer and establishes a cross-modal self-training framework, which iterates between parameter updating and pseudolabel estimation.
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- LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment29
This work proposes LCPS, the first LiDAR-Camera Panoptic Segmentation network that integrates both geometric and semantic fusion information for the entire point cloud.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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